Cost-effectiveness of Increasing Buprenorphine Treatment Initiation, Duration, and Capacity Among Individuals Who Use Opioids

Key Points Question Are interventions to increase buprenorphine treatment initiation, duration, and capacity for treating opioid use disorder cost-effective? Findings In this quality and economic evaluation of modeled interventions to increase buprenorphine treatment initiation, duration, and capacity, we projected health outcomes and costs of 5 interventions and their combinations. This study found that a portfolio of interventions, including contingency management, hub-and-spoke clinician training, initiation of buprenorphine treatment in the emergency department, and telehealth, was the preferred strategy for a range of generally accepted willingness-to-pay thresholds. Meaning The findings of this study suggest that interventions that increase buprenorphine treatment duration concurrently with treatment capacity are the most cost-effective and have the largest association with reducing overdose deaths.

eMethods 1: Overview of SOURCE model SOURCE (1,2) tracks opioid misuse, including OUD involving prescription opioids and heroin. SOURCE models the three FDA-approved MOUDs: methadone, buprenorphine, and extended-release injectable naltrexone. Each medication varies in the average duration of treatment, treatment capacity, and treatment-seeking rate. In SOURCE, mortality is based on the type of opioid use, likelihood of fentanyl exposure associated with heroin use, probability of naloxone administration, treatment status, and remission status.
Dynamic feedback loops are a key feature of SOURCE and of the cost-effectiveness analysis as they capture the interactions between the supply of and demand for MOUD. These feedback loops affect transition rates in the model and include social influence factors, risk perception, capacity limits on treatment, and availability of prescription and illicit opioids. For instance, capacity limits on buprenorphine prescribers and other systemic barriers to prescribing reflect real-life limits of buprenorphine provision (3). SOURCE includes additional time-dependent trends as inputs. For example, SOURCE reflects that fentanyl contamination in the heroin supply as well as the availability of naloxone, an opioid overdose reversal medication, have increased over time. These trends affect the risk of mortality in the event of an opioid-related overdose. SOURCE does not consider overdose deaths that are unrelated to intentional opioid use and does not specify the number of overdoses resulting from fentanyl contamination of non-opioid substances. SOURCE was calibrated to data , including target data from the National Survey on Drug Use and Health, the National Vital Statistics System, and IQVIA prescription data-see (1,2).
In SOURCE, buprenorphine reduced the hazard of overdose death while in treatment (2). Also, the average duration is linked to treatment success based on the assumption that the longer the treatment duration, the greater the probability of exiting treatment into remission rather than returning to OUD. eFigure 1 presents the overview of SOURCE. Details about data inputs, assumptions, and validation are reported elsewhere (1,2).

eMethods 2: Description of literature review process
For each intervention, the review included specific search terms and processes (eTable 1). Two researchers (AFZ, ZR) conducted a review of the titles and abstracts, completed a full-text review of the papers that met the search criteria, extracted results on effectiveness and costs for each intervention, and discussed their results with a third researcher (ALC).
After reviewing the titles of all initial search results, we reviewed 180 titles and abstracts, and 45 complete manuscripts (eTable 1). We aggregated the quantitative results for the effectiveness of interventions based on trials, retrospective data review, and modeling studies and presented them in tables to compare similar values (eTables 2-6). Title and abstract reviews were conducted in Rayyan systematic review software (4).
The values selected for the base-case effectiveness for each intervention are highlighted below.
Inclusion Criteria for the literature search included: • Quantitative measures of policy-effectiveness or costs • Treatment for opioid use disorder included in the study • Clearly about opioid use disorder • Contains buprenorphine prescribed for opioid use disorder (OUD) • Intervention was the primary part of the intervention • Includes ages ≥ 18 • Peer-reviewed journal article • In English or translated to English

Intervention effectiveness
We selected the final values for the effectiveness of interventions based on whether we could implement these findings into the variables of SOURCE and the robustness of methods for estimating the effectiveness of interventions before and after implementation. For example, Fairley et al. (5) conducted a meta-analysis to calculate the hazard rate ratios of contingency management (HR=0.594) and psychotherapy (HR=0.986) and included annual costs (5). For the hub and spoke model, we used the Vermont hub and spoke results for our effectiveness measures because they studied the change in the number of prescribers and the number of patients per prescriber before and after program implementation (6). To model the effectiveness of ED initiated buprenorphine, we considered the percent of people who were still enrolled in buprenorphine after 30 days in four studies (7)(8)(9)(10) and compared those to the control 30-day treatment retention in D'Onofrio to calculate a mean, minimum, and maximum treatmentseeking ratio of ED initiation (1.556 [1.164, 2.108]). ED initiation effectiveness was applied to the number of people in the ED, which we estimated as the percent of people per year with OUD involving prescription opioids and/or heroin (with a model-estimated probability of exposure to fentanyl) who would experience a non-fatal opioid overdose. This was estimated to be 26% in the SOURCE model run of the status quo.
The total number of people to receive the ED initiation intervention at time t is modeled as: = number of people who receive ED-initiated buprenorphine at time t Increase in average duration of buprenorphine prescribing while in telehealth was calculated using the average duration with and without telehealth in the Vakkalanka study.

Intervention costs
Annual costs for contingency management services and psychotherapy were derived in Fairley, et al. (5). We calculated the cost of buprenorphine per provider ($31,593, 2017 USD), by dividing the Washington-state hub and spoke program maximum annual contract per network ($789,825, 2017 USD) by the average number of providers per network (n=25) and adjusting for inflation (12). For ED initiation, we summed the screening cost ($214, 2017 USD) and intervention costs ($83, 2017 USD) from Busch et al. 2017 (13) plus a cost of additional counseling services (14) ($225, 2020 USD), meant to represent a bridge program from buprenorphine in the ED to linkage with a longer-term provider. We did not find the cost of telehealth for buprenorphine prescribing. However, for all results of cost of telehealth for other types of appointments, the cost of a telehealth appointment was less than for in-person appointment (eTable 7). Therefore, we assume the cost of telehealth is $0 compared to in-person appointments. We did not consider fixed technology installment costs, which is consistent with other cost-effectiveness analyses (15). Additional, initial costs for scale-up of interventions were not included. All costs were adjusted to 2021 U.S. Dollars using the Consumer Price Index.

eMethods 4: Methods for calculating expected end-of-life QALYs
For opioid overdose deaths, we calculated the estimated lifetime QALY lost by applying agespecific mean quality of life values estimated in Sullivan and Ghushchyan 2006 (16) to the expected remaining life years based on the age-specific annual probability of death according to CDC life tables. We discounted future QALYs at 3% annually. The age-weighted average of end-of-life QALYs was calculated using the estimated age-stratified population of people with OUD in the United States (17).

Background healthcare costs
We used average baseline healthcare cost estimates from Medical Expenditure Panel Survey (MEPS) (18) The excess healthcare cost of the population with OUD is obtained from (19) separately for the treated and untreated subpopulations and without including the treatment cost of OUD. We conservatively assumed that patients with OUD who are receiving treatment have 20% higher costs on average than those not receiving treatment, which also aligns with other studies in the literature (5,19,20).

Cost of non-fatal and fatal opioid overdose
Opioid overdose cost consists of the expected costs of calling Emergency Medical Services (EMS) to transport to a hospital, ED visit, and hospital stays. We assumed that 90% of the opioid overdose cases are transported to the hospital by EMS (21), and the costs of calling EMS and transport to the hospital were obtained from (21)(22)(23). We estimate that 73% of non-fatal opioid overdoses are discharged from ED versus the remaining are discharged after inpatient stays (21). Whereas 1.4% of fatal opioid overdoses are observed in ED and 6.2% during an inpatient stay (24). Varying levels (low, medium, high) of cost estimates for ED (21,22,25) inpatient stay (21,22,24) and EMS (21)(22)(23) are obtained from the literature.

MOUD costs
Methadone treatment: Treatment cost includes the cost of methadone and associated drug administration cost. We assumed that methadone is administered daily for patients' treatment programs.
Buprenorphine treatment: Treatment cost includes the cost of buprenorphine, drug monitoring, and counseling services. We assumed that the patient would receive medication counseling twice a week.
Naltrexone treatment: Treatment cost includes the cost of injectable naltrexone, administration, and related services (e.g., drug monitoring, counseling services, etc.). We assumed that the patient would administer naltrexone and receive medication counseling services once per month.

Expected remaining life years lost due to overdose
Following the recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine (26) we included the loss of future productivity minus their future consumption as their net productivity loss for each overdose death. We first calculated the age-specific expected remaining life years lost using CDC life tables (27)

Productivity
We used the mean income by age group as estimated in the U.S. Census Bureau, Current Population Survey, 2020 Annual Social and Economic Supplement (CPS ASEC) (28).

Consumption
We used the age group-specific mean expenditure per person as estimated in the Consumer Expenditure Survey, U.S. Bureau of Labor Statistics from September 2020 (29) To calculate the net present value, we calculated net productivity (productivity minus consumption) for each year, multiplied by the probability an individual would have lived to that year from the life tables and discounted all future values by 3%.

Age-weighted average
Age-weighted averages for consumption and productivity were calculated using the estimated age-stratified population of people with OUD in the United States (17) and adjusted for inflation to reflect 2021 USD using the Consumer Price Index. We assumed productivity and consumption costs were equal for individuals experiencing OUD and those not experiencing OUD, similar to Appendix A in the Second Panel on Cost-Effectiveness in Health and Medicine (30).

eMethods 7: Sensitivity analysis process
We conducted a probabilistic sensitivity analysis of intervention effectiveness by running 1,000 simulations for each intervention or combination of interventions using effectiveness parameters and intervention and treatment costs, sampled from uniform distributions over the uncertainty ranges, when available (Table 1, eTable 8). For input and treatment costs, we assumed a range that was 25% above and below each basecase value.
We calculated the net monetary benefit (NMB) for each run for intervention to consider if they were considered cost-effective compared to the status quo (NMB > 0).

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We then reviewed the percent of runs in which each intervention or combination of interventions was considered cost-effective compared with the status quo of no additional interventions as well as the percent of runs in which each intervention was the preferred intervention (NMB the highest for the intervention compared all other interventions for that simulation run).